Triple
T29356146
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Naha–Tokyo Haneda |
E744447
|
entity |
| Predicate | isWithinSingleCountry |
P166672
|
FINISHED |
| Object | true |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: true | Statement: [Naha–Tokyo Haneda, isWithinSingleCountry, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isWithinSingleCountry Context triple: [Naha–Tokyo Haneda, isWithinSingleCountry, true]
-
A.
isUniqueWithinCountry
Indicates that an entity is the only one of its kind within the boundaries of a specific country.
-
B.
existsInConstituentCountry
Indicates that one entity is located within or is present in a country that is a constituent part of a larger sovereign state.
-
C.
isSetInCountry
Indicates that something (such as an event, story, or scene) takes place within the geographical or political boundaries of a specified country.
-
D.
meetsInCountrySubdivision
Indicates that two or more entities meet or have a meeting within a specific administrative subdivision of a country (such as a state, province, or region).
-
E.
meetsInCountry
Indicates that two or more entities have an in-person meeting that takes place within the specified country.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f0a79aee588190b490f19d93c6e52d |
completed | April 28, 2026, 12:27 p.m. |
| NER | Named-entity recognition | batch_69f66984ecf48190bd8ccfcfee1c55af |
completed | May 2, 2026, 9:15 p.m. |
| PD | Predicate disambiguation | batch_69f660f4f7a88190b93c60d76b86c912 |
completed | May 2, 2026, 8:39 p.m. |
| PDg | Predicate description generation | batch_69f661b47d088190934f63884a203261 |
completed | May 2, 2026, 8:42 p.m. |
Created at: April 28, 2026, 2:12 p.m.